The Impact of Exchange Rates on Inflation in Nigeria (1981 ...the exchange rate and GDP growth....

Management Studies and Economic Systems (MSES), 4 (3), 171-195, Summer 2019 © ZARSMI

The Impact of Exchange Rates on Inflation in Nigeria (1981-2015)

* 1Charles Odinakachi Njoku, 2 Chilaka Emmanuel Nwaimo

1,2 Department of Financial Management Technology, School of Management Technology, Federal University of Technology, Owerri, Imo State, Nigeria

Received 18 March 2019, Accepted 9 April 2019

ABSTRACT: This study examines the impact of exchange rate on inflation in Nigeria over the period of 1981-2015. Secondary data were collected from the CBN statistical Bulletin World Bank Data File and those of the Federal Bureau of Statistics in order to establish the relationship between the dependent variable (inflation rate) and the independent variables (exchange rate, Non-oil export, and money supply). The research adopted the Vector Error Correction Mechanism (VECM) and the results of the analysis show that that the fluctuating exchange rate has significantly impacted on the persistence inflation that the country has witnessed. As high exchange rate has led to imported inflation as such the monetary authority in their quest to curb inflation should not totally rely on this instrument to control inflation, but should use it to complement other macro-economic policies. The research recommends that efforts should be intensified to increase the volume of non-oil export to make up for the extra demand for foreign exchange that may be created by the depreciation of Naira. Keywords: Economy, Inflation, Exchange rates, Economic growth INTRODUCTION

One of the key challenges to policy management everywhere the planet and notably in rising and developing economies has been the result of changes in exchange rates on inflation and economic activities. It is believed that rate of exchange movements would produce domestic economic distortions and have an effect on a country's economic aggressiveness. The hurtful result of rate of exchange placement is well documented within the literature and there's usually reluctance on the aspect of policymakers to regulate exchange rates, thanks to the perceived negative effect on the economy, mainly due to pass-through effects. Mehdi (2014) state that the effect of exchange rate on inflation rate varies in different countries asserting that one of the factors determining the way exchange rate

affects inflation rate is the development level of each country's financial markets revealing that new theories emphasize the high correlation between economic growth and innovation.

Exchange rate influence domestic prices through their effects on aggregate supply and demand. In general, once a currency depreciates it'll end in higher import costs if the country is a global value taker, whereas lower import costs result from appreciation. The potentially higher value of foreign inputs related to rate of exchange depreciation will increase marginal prices and ends up in a better value of domestically made merchandise (Kandil, 2004). Also, import-competing companies may increase costs in response to foreign rival value will increase to *Corresponding Author, Email: [email protected]

Njoku, Charles Odinakachi; Nwaimo, Chilaka Emmanuel

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boost profit margins. However, the extent of such value adjustment depends on a spread of things like market structure, the relative number of domestic and foreign firms in the market, the nature of government exchange rate policy and product substitutability (Fouquin, 2001).

The problem of a way to cut back inflation has been a central issue among policymakers since the Nineteen Seventies. Although obtainable knowledge show that the Nigerian economy has on the common veteran moderate inflation within the pre-SAP amount, the unfavorable consequences of inflation have since assumed an intolerable dimension since the period of SAP and after SAP until the present day. Inflation as in the fourth quarter of 2016 was at 18.67% from 8.7 % it was in 2015 (NBS, 2016). Several authorities have attributed it to the enlargement of public expenditure arising from the rise in oil revenue, which culminated into a vast expansion of aggregate demand and the inelastic supply of domestic output.

The rapid climb in funds arising from the proof of oil earnings conjointly exerted AN upward pressure generally indicant. When the price of crude oil slumped in 2015, Nigeria’s crude oil, which sold at slightly above US$100 per barrel in 2014, fell to less than US$50 by August 2016. This triggered off a series of developments in the economy (Oleh, 2015). One example of such developments is the state of fiscal crisis as reflected in the persistent budget deficit, which culminated to approximately N44.88 billion in 2015 (CBN 2015). Monetary policy became extremely expansionary as an outsized a part of the deficit incurred throughout this era were supported through the creation of credit therefore the native domestic credit to the economy and most of the increase was attributable to the net claim by the government. However, the rate of inflation in 2016, which stood above 18% is often explained in terms of acute shortage of imported goods and services imposed by inadequate foreign exchange earnings, a derivation of the steep fall in crude oil prices (Gabriel and Ujah, 2015).

In addition to the on top of, the frequent fiscal deficit operation in the last two decades in which budget deficit is financed through banks has further exerted upward pressure on the general price level. This suggests that this inflation might are caused by these factors. While the channels

through that rate of exchange depreciation have an effect on costs are accepted, the extent to that this development engenders value inflation in Nigeria is one in all the explanations for the study.

The fluctuation in export earnings due to fall and rise in the oil price in the international market has led to the problem of exchange rate fluctuation. Nigeria government despite adopting a series of exchange rate policies has failed to keep the naira value stable over time. Nigeria currency keeps on falling in relation to the dollar. The current decline in the value of naira has led to a series of economic problem like economic recession, high inflation, high unemployment among other macroeconomic problem.

While some social scientist disputes the power of modification within the real charge per unit to enhance the balance of developing countries (Hinkle, 1999) thanks to snap of their low export, others believe that structural policies might, however, modification the long-run trends within the terms of trade and therefore the prospects for export-led growth. Instabilities of the foreign exchange rate are also a problem to the economy as it determines the value of the country currency in acting as a medium of exchange. The main thrust of this study is to evaluate the impact of exchange rate on inflation in Nigeria over the period of 1981-2015.

Literature Review

Several empirical studies that have undertaken to spot the attainable determinants of inflation in African nation et al. have known charge per unit as another inflation deciding variable. Honoham and Lane (2003) for instance, reported a variety of regressions, explain annual inflation differentials across the Eurozone over the period 1999-2001, and found a substantial role for the variation in nominal effective charge per unit movements in explaining divergent inflation rates. Honoham and Lane (2004) in a related study confirm that exchange rate matter for EMU inflation rates during the periods of Euro appreciation (2002-2003) as well as during the periods of Euro depreciation (1999-2001). Aigbokhan(2013) showed that the level of the real exchange rate was a primary determinant of the rate of inflation in Mexico during the 1980s and 1990s.

Chhibber (1991) developed in depth political economy model, which takes into account both

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monetary and structural factors while investigating the causes of inflation in Zimbabwe. Their investigation shows that financial growth, foreign price, exchange rate, interest rate, unit labor cost, and real income, are the chief determinants of inflation in this country. A similar macroeconomic model of inflation was employed for Ghana by Chhibber and Shafik (2005). This study, that lined the amount 1965–1988, suggests that the growth of money supply is one key variable explaining the Ghanian inflationary process. Such variables as the official exchange rate and real wages could not exert any significant influence on inflation. However, a significant positive relationship was found between the parallel exchange rate and the general price level. Still, on the difficulty of inflation, Chhibber planned that there's only 1 relationship between charge per unit and value inflation. Basing his argument on empirical studies of some African countries, one of his main conclusions is that devaluation could exert upward pressure in the general price level through its increased cost of production in the short-run. Owoeye and Ogunmakin (2013) using two proxies for bank performance (loan loss to total advances ratio and capital deposit ratio) examined the impact of unstable exchange rate on bank performance in Nigeria. Their specified models suggest that the impact of exchange rate on bank performance is sensitive to the type of proxy used to capture bank performance. Loan loss to total advance quantitative relation shows that unsteady charge per unit might have an effect on the power of lenders to manage loans ensuing into a high level of unhealthy loans whereas capital deposit ratio does not have a significant relationship with the exchange rate.

Dada and Oyeranti (2012) examine the exchange rate and macroeconomic aggregates in Nigeria. The result shows that there is no evidence of a strong direction between changes in the exchange rate and GDP growth. Rather, the country's growth has been directly affected by fiscal and monetary policies and other economic variables particularly the growth of exports which is major oil. In short, the nature of the effect of exchange rate volatility on investment and growth is yet unresolved. There is, therefore, the need for more empirical research on the subject matter. This is significantly necessary visible of the

character of charge per unit in developing countries like African nation.

Ettah (2011) focused on the effects of price and exchange rate fluctuations on Agricultural exports (cocoa) in Nigeria. Data were applied to an export supply function for cocoa specified and estimated using the Ordinary Least Squares Regression. Results showed that charge per unit fluctuations and agricultural credits completely have an effect on cocoa exports in African nation. Results also revealed that relative prices of cocoa are insignificantly related to the quantity of export. Their result, therefore, implied a positive significant effect of exchange rate volatility on cocoa exports in Nigeria. They recommend that agricultural credit schemes should be restructured in a way that should meet the needs of farmers, and such credit facilities should be made available and accessible to cocoa farmers in order to boost their production capacity while there should be a free market determination of exchange rate for export of cocoa in Nigeria.

Bakare (2011) supports the replacement of the floating exchange regime while adopting purchasing power parity which has been considered by past studies to be more appropriate in determining realistic exchange rate for Nigerian monetary unit and contribute completely to political economy performances in African nation.

In another work, Omojimite and Akpokodje (2010) investigated the effect of exchange rate reforms on Nigeria’s trade performance during the period 1986-2007. The study found a minimal positive effect of exchange rate reforms on non-oil exports through the depreciation of the value of the country’s currency. It was additionally found that the structure of imports that is pro-consumer merchandise remained unchanged even when the adoption of charge per unit reforms. Exchange rate reforms were found to not constrain imports as anticipated. Rather, they stimulate imports, albeit insignificantly. These authors suggest that exchange rate reforms are not sufficient to diversify the economy and change the structure of imports.

Aliyu (2009) examined the exchange rate pass-through in Nigeria for the period 1986 to 2007. Quarterly series was employed and a Vector Error Correction Model estimation was used in the estimation process. The authors found


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that charge per unit pass-through in African nation throughout the amount into account was low and declined at the side of the worth chain, which partly overturns the conventional wisdom in the literature that charge per unit pass-through is often significantly higher in developing countries than developed countries. The authors conclude that within the long-term, have would seemingly increase and financial policy ought to be designed to accommodate the impact.

Another study by Mireille (2007) argues that overvaluation of exchange rates has official a serious black eye within the recovery method of African nation and Benin Republic. In addition, the author suggests that devaluation among well-targeted measures aboard AN upward adjustment within the domestic worth of tradable product, could restore exchange rate equilibrium and improve economic performance.

The few studies on contractionary devaluation supported multivariate analysis embody those of Edwards (1989), Agénor (1991), and Morley (1992). In a pool-time series/ cross-country sample, Edwards (1989) regressed the $64000 GDP on nominal and real exchange rates, government spending, the terms of trade, and measures of money growth. He found that devaluation tended to reduce the output in the short term even where other factors remained constant. His results for the long impact of a true devaluation were a lot of mixed, however as an entire, it was suggested that the initial contractionary effect was not reversed subsequently. In the same manner, Agénor (1995) employing a sample of twenty-three developing countries, regressed output growth on contemporaneous and lagged levels of the real exchange rate and on deviations of actual changes from expected ones within the real rate, government disbursement, the money supply, and foreign income. The results showed that surprises in the real exchange rate depreciation actually boosted output growth, but that depreciation of the level of the real exchange rate exerted a contractionary effect.

Odusola and Akinlo (2001) examined the linkage among exchange rate, inflation, and output in Nigeria. A structural power unit model was utilized that captured the interactions between rate and output. Evidence from the contemporaneous models showed a contractionary impact of the parallel rate on output solely within

the short term. Prices, parallel rate, and lending rate were found to be important sources of perturbations in the official exchange rate. In addition, output and parallel rate were important determinants of inflation dynamics in African nation. The authors ended by suggesting a lot of conjunct efforts by the financial organization towards taming the parallel rate behavior and formulating financial policies that enhance financial gain growth. Largely the findings were informative. Batini (2004) and Mordi (2006) present similar arguments in different studies in Nigeria. On the contrary, Aliyu (2009) find that prices react less proportionately to exchange rate shock in Nigeria.

Mauna and Reza (2001) studied the effect of trade liberalization, real exchange rate and trade diversification on selected North Africa countries Morocco, Algeria, and Tunisia. By rotten in real rate into basic and financial determinants, and by using both standard statistical measures of exchange rate fluctuation and the measures of exchange rate risk developed by Puree and Steinher (1989), they reached the conclusion that exchange rate depreciation has a positive effect on the quantity or manufactured exports while exchange rate misalignment, volatility or fluctuation has a negative effect. According to them, the motivating result is that all manufacturing sub-sectors are responsive to exchange rate change but the degree of responsiveness differs across sectors.

Broda and Romails (2003) found that real exchange rate volatility depresses trade in differentiated goods. The study used bilateral trade model, where the oils (ordinary least square) and GMM (Generalized method of the moment) methods were used. After taking into consideration the direction of relation, they ascertained that a 10 percent increase in volatility depresses differentiated product trade by 0.7 percent, while a 10 percent increase in trade reduces rate volatility by zero.3 percent. Their OLS calculable results showed that the impact or volatility on trade is reduced by 70 percent. They even the result by tilt that a lot of of the correlation between trade and alter to the impact that trade has in depressing fluctuation. Their study further revealed that a 10 percent increase in the intensity of bilateral trading relationship reduces the volatility if the associated exchange rate by 0.3 percent.


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RESEARCH METHOD Model Specification

From the review of the theoretical framework, we specify our model. This study is anchored on the monetary theory of exchange rate which is predicated on the importance of money. It identifies- the exchange rate as a function of the relative shift in the money stock. It is important to know that the Purchasing Power Parity (PPP) is a major component of the monetary approach. Monetary approach in the theory exchange rate determination views the exchange rate as being the relative prices of two assets (national monies) is determined primarily by the relative supplies of and demand for those monies and that the equilibrium exchange rate is attained when the existing stocks of the two monies are willingly held (Gbosi, 2003:105), the model is specified below as follows.

INF = f (EXR, NOX, MS) 1 The model uses Inflation rate (INF),

Exchange of US Dollar to Nigeria’ Naira (EXR), Non-oil export (NOX) and money supply (MS) as the specific objective is to examine the impact of exchange rate fluctuations on inflation in Nigeria. Thus, the model specified below is used to estimate the relationship between exchange rate fluctuation and inflation rate with the aid of linear equation. The mean equation is given by:

INF = c + β1EXRt + β2NOXt +β3MSt +ut Model Estimation

In this study, the following tests shall be conducted:

Unit root test Co-integration test Vector Error Correction Mechanism Unit Root Test: It is used to test for the

stationarity of the time series data in order to avoid spurious regression results. Augmented Dickey fuller will be used in the process. Cointegration Test: It is used to test for the long run relationship between the variables. And a protracted run relationship is found on these variables during which we are going to study. Johansen Co-integration Approach will be undertaken by the researcher in the course of the analysis.

Vector Error Correction Mechanism (VECM): The purpose of the error correction model is to indicate the speed of adjustment from the short-run equilibrium to the long-run equilibrium state. The bigger the constant of the parameter, the upper the speed of adjustment of the model from the short-term to the long-term equilibrium.

Sources of Data

Secondary data comprising of data of the above-discussed variables were used. These variables were collected from the Central Bank of Nigeria Statistical Bulletin, World Bank Data File and those of the Federal Bureau of Statistics. Its period of coverage spanned from 1981-2015. All the variables were measured in monetary terms using Nigeria's currency (Naira). The study captured the systematic annual time series of the considered variables in the model specification. Data Presentation and Analysis of Results

The attempt to study the relationship between inflation and exchange rate in Nigeria led the researcher to subject the data collected to Unit Root, Cointegration, and Vector Error Correction. The variables considered in this research work are: Inflation Rate (INF) (dependent variable) and the independent variables include, Exchange rate, Non-Oil Export, and Money Supply. The empirical results are presented below: Unit Root Test

In other to test for the presence or absence of unit root in the data used for the empirical analysis, the Augmented Dickey-Fuller (ADF) test was employed and the test result is as presented below:


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Table 1: Unit Root Test Result

Variable Level ADF 5% VALUE CV

1st difference ADF 5% VALUE CV

ORDER OF INTEGRATION

REMARKS

INF -3.102490-3.548490 -5.839618-3.552973 I(1) Stationary EXR -2.556946-3.548490 -5.754111-3.552973 I(1) Stationary NOX MS

-1.661517-3.548490 2.347712 -3.548490

-4.215383-3.552973 -3.803120 -3.552973

I(1) I(1)

Stationary Stationary

Source: Researcher’s Computation (See Appendix)

Table 2: Cointegration Result Trend assumption: Linear deterministic trend

Series: INF EXR NOX MS Lags interval (in first differences): 1 to 1

Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.545746 53.93312 47.85613 0.0121 At most 1 0.462066 27.89289 29.79707 0.0816 At most 2 0.196836 7.432258 15.49471 0.5279 At most 3 0.006006 0.198781 3.841466 0.6557

Trace test indicates 1 cointegratingeqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Source: Researcher’s Computation (See Appendix)

Table 1 above, showed that none of the

variables was stationary at level as there ADF values (3.102490, 2.556946, 1.661517 and 2.347712) were less than 0.05 critical value (3.548490, 3.548490, 3.548490 and 3.548490), but at first differencing all the variables (INF, EXR, NOX and MS) became stationary as their ADF values (5.839618, 5.754111, 4.215383 and 3.803120) became greater than their 0.05 critical value (3.552973, 3.552973, 3.552973 and 3.552973). These indicate that all the variables were stationary and integrated at first difference, that is order 1, I(1). Since the variables are not integrated at level but were all integrated at the same level at order one, we, therefore, proceed to conduct cointegration test and short-run speed of adjustment from long-run disequilibrium.

Cointegration Test

Empirical results from the Johansen cointegration analysis are presented in table 2 below. The Johansen’s check is geared toward

crucial whether or not a long relationship exists between the series, and start with the null hypothesis that there is no cointegrating relation. We test that there is at least one cointegrating equation. Since there are four variables in the model, we then test whether the number of cointegrating equations is zero, one, two, three or four.

The results of the cointegration in the table above indicated that the trace statistics is greater than the critical value at 5 percent level of significance in one of the equations. This shows that there is a cointegrating relationship among the variables used to model the relationship between Inflation and Exchange Rate in Nigeria for the period under study. Specifically, they are 53.93312>47.85613. Also, the p-value is less than 0.05 (0.0121). In alternative words, the null hypothesis of no cointegration among the variables is rejected. Hence, the test result shows the existence of a long-run relationship in one cointegrating equation at 5% significance level.


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The normalized cointegrating coefficients for one co-integrating equation given by the long-run relationship is

INF= 0.068230EXR+ 0.651669NOX- 0.053798MS (0.13527) (0.20529) (0.01576)

Where INF is the dependent variable, 0.068230 is the coefficient of EXR while 0.651669 is the coefficient of NOX and -0.053798 is the coefficient of Money Supply. The sign borne by the adjusted coefficient estimates of EXR and NOX is positive while that of M2 is negative. This implies that the long run relationship between Inflation, Non-Oil Export, and the Exchange rate is positive while that of the money supply is negative. This conforms to apriori expectation of the relationship between inflation and exchange rate Vector Error Correction Model (VECM)

In view of the presence of a cointegrating vector among the variables as evidenced by the cointegration tests, VECM will be conducted to check the speed of adjustment from short-run dynamics to their long-run static disposition:

From the table ECM (1) was consistent by assuming a negative value. It suggests that the ECM could correct any deviation from the long-run equilibrium relationship between INF and the explanatory variables. The coefficient indicates a speedy adjustment of 16.9% per annum. This implies that following short-run disequilibrium, 16.9% of the adjustment to the long-run takes place within one year. The above result shows that the R2 is 0.74, which shows that the model explains about 74% of the total variations in INF are explained by the independent variables during the period of the study while the remaining 26%

is explained by a variable not included in the model.

The result also shows that exchange rate and lag inflation by one were statistically significant considering its probability value which was less than 0.05, but for NOX and MS the p-values were greater than 0.05 meaning that they are statistically insignificant. At 2.00; the Durbin-Watson statistics suggest evidence of no serial auto-correlation. This is an indication that the dependent variable (INF) is well explained by the independent variables.

Implications of the Study

The VECM result above showed that of all the explanatory variables used in the model, only the exchange rate was statistically significant and had a negative impact on inflation. Non-Oil export had a positive impact but was not statistically significant, so as money supply which negates the apriori expectation was also not statistically significant. The general observation from the estimates so far is that the literal notion that inflationary pressure in Nigeria has been much more occasioned by the expansionary monetary policy is not justified, as exchange rate fluctuation has also contributed significantly to the persistence increase in the general price level, as an increase in exchange rate has ultimately resulted in high cost of imported goods (Finished goods and Raw materials).

The R- square which was 74% and depicts goodness of fit as it shows that the explanatory could explain about 74% of the fluctuation in the inflation rate. The coefficient of the ECM also was consistent by assuming a negative value of -0.169 indicating that a deviation in the inflation rate from equilibrium is corrected by as low as 16.9 percent the following year.

Table 3: VECM Result

Variable Coefficient Std. Error t-Statistic Prob

C 10.88961 5.75456 1.89234 0.0611 D(INF(-1)) 0.133729 0.02885 4.63532 0.0000 D(EXR(-1)) -0.537755 0.25157 -2.13758 0.0348 D(NOX(-1)) 0.055020 0.05280 1.04211 0.2997 D(MS(-1)) -0.018296 0.01155 -1.58433 0.1160 ECM(-1) -0.169428 0.10625 -1.59468 0.1137

R-squared = 0.74 F-statistic = 1.67 Durbin-Watson =2.00 SOURCE: Researcher’s Own Computation (See Appendix II)


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CONCLUSION This study has shown that the fluctuating

exchange rate has significantly impacted on the persistence inflation that the country has witnessed. As high exchange rate has led to imported inflation as such the monetary authority in their quest to curb inflation should not totally rely on this instrument to control inflation, but should use it to complement other macro-economic policies. More so, policies should be put in place to increase the domestic production of export commodities, which are currently short-supplied. The negative effect of the exchange rate has led to the persistence of inflation. In fact, the naira exchange to $1.00 is N450.00 at the parallel market instead of the official rate of N350.00. This is as a result of the naira being cheaper compared to the dollar. The demand for dollar has remained so high, hence the increase in the exchange rate and ultimately resulting in the high cost of imported goods cum imported inflation. RECOMMENDATIONS

The results show that inflation in Nigeria is highly responsive to exchange rate depreciation, indicating that the model has a self-adjusting mechanism for correcting any deviation of the variables from equilibrium.

1. The government should make the economic investment friendly by putting in place political stability, security of lives and good economic climate to draw home foreign investors to boost the nation's productivity. This will also reduce capital flight plaguing the country.

2. The government should be serious with its economic reforms i.e Small and Medium Enterprises Equity Investment Scheme (SMEEIS) and others as this will enhance and promote the production of local goods, reduce pressure on imported goods which will automatically reduce the demand for the dollar. This would lead to a favorable exchange rate for the country

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181

APPENDIX

Data for Regression Analysis

YEAR INF (%)

EXR (N : $)

NOX (N’ BILLION)

MS (N’ BILLION)

1981 20.555 0.6100 0.3 14.47 1982 5.882 0.6729 0.2 15.79 1983 22.222 0.7241 0.3 17.69 1984 40.909 0.7649 0.2 20.11 1985 3.226 0.8938 0.5 22.30 1986 6.25 2.0206 0.6 23.81 1987 11.765 4.0179 2.2 27.57 1988 34.211 4.5367 2.8 38.36 1989 49.02 7.3916 3.0 45.90 1990 7.895 8.0378 3.3 52.86 1991 12.195 9.9095 4.7 75.40 1992 44.565 17.2984 4.2 111.11 1993 57.143 22.0511 5.0 165.34 1994 57.416 21.8861 5.3 230.29 1995 72.729 81.0228 23.1 289.09 1996 29.291 81.2528 23.3 345.85 1997 10.673 81.6494 29.2 413.28 1998 7.862 83.8072 34.1 488.15 1999 6.618 92.3428 19.5 628.95 2000 6.938 101.7740 24.8 878.46 2001 18.869 111.4872 28.0 1,269.32 2002 12.883 120.6528 94.7 1,505.96 2003 14.033 129.2230 94.8 1,952.92 2004 15.001 133.0008 113.3 2,131.82 2005 17.856 131.1004 106.0 2,637.91 2006 8.218 128.1420 133.6 3,797.91 2007 5.413 125.0660 199.3 5,127.40 2008 11.581 117.7823 525.9 8,008.20 2009 12.543 147.2718 500.9 9,411.11 2010 13.72 148.3100 711.0 11,034.94 2011 10.841 151.8269 913.5 12,172.49 2012 12.217 155.4502 879.3 13,895.39 2013 8.476 155.2537 1,130.2 15,160.29 2014 8.057 156.4848 953.5 17,679.29 2015 9.00 191.8035 660.7 18,901.30


182

Regression Results

Unit Root

INF @ LEVEL

Null Hypothesis: INF has a unit root Exogenous: Constant, Linear Trend Lag Length: 0 (Automatic - based on SIC, maxlag=0)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -3.102490 0.1219 Test critical values: 1% level -4.252879

5% level -3.548490 10% level -3.207094

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation Dependent Variable: D(INF) Method: Least Squares Date: 11/13/17 Time: 17:30 Sample (adjusted): 1982 2015 Included observations: 34 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

INF(-1) -0.469889 0.151455 -3.102490 0.0041 C 14.30462 6.790297 2.106627 0.0433

@TREND("1981") -0.302123 0.268952 -1.123331 0.2699

R-squared 0.237691 Mean dependent var -0.339853 Adjusted R-squared 0.188509 S.D. dependent var 16.29147 S.E. of regression 14.67581 Akaike info criterion 8.294376 Sum squared resid 6676.762 Schwarz criterion 8.429054 Log likelihood -138.0044 Hannan-Quinn criter. 8.340305 F-statistic 4.832949 Durbin-Watson stat 1.714594 Prob(F-statistic) 0.014895


183

INF @ 1ST Difference

Null Hypothesis: D(INF) has a unit root Exogenous: Constant, Linear Trend Lag Length: 0 (Automatic - based on SIC, maxlag=0)

t-Statistic Prob.*


5% level -3.552973 10% level -3.209642


Augmented Dickey-Fuller Test Equation Dependent Variable: D(INF,2) Method: Least Squares Date: 11/13/17 Time: 17:31 Sample (adjusted): 1983 2015 Included observations: 33 after adjustments


D(INF(-1)) -1.049211 0.179671 -5.839618 0.0000 C 2.614058 6.256433 0.417819 0.6791

@TREND("1981") -0.141012 0.307378 -0.458756 0.6497

R-squared 0.532415 Mean dependent var 0.473212 Adjusted R-squared 0.501242 S.D. dependent var 23.79071 S.E. of regression 16.80166 Akaike info criterion 8.567341 Sum squared resid 8468.878 Schwarz criterion 8.703387 Log likelihood -138.3611 Hannan-Quinn criter. 8.613116 F-statistic 17.07970 Durbin-Watson stat 1.960231 Prob(F-statistic) 0.000011


184

EXR @ Level

Null Hypothesis: EXR has a unit root Exogenous: Constant, Linear Trend Lag Length: 0 (Automatic - based on SIC, maxlag=0)

t-Statistic Prob.*


5% level -3.548490 10% level -3.207094


Augmented Dickey-Fuller Test Equation Dependent Variable: D(EXR) Method: Least Squares Date: 11/13/17 Time: 17:32 Sample (adjusted): 1982 2015 Included observations: 34 after adjustments


EXR(-1) -0.318297 0.124483 -2.556946 0.0157 C -6.931562 5.408977 -1.281492 0.2095

@TREND("1981") 2.072840 0.761436 2.722277 0.0105



185

EXR @ 1ST Difference

Null Hypothesis: D(EXR) has a unit root Exogenous: Constant, Linear Trend Lag Length: 0 (Automatic - based on SIC, maxlag=0)

t-Statistic Prob.*


5% level -3.552973 10% level -3.209642


Augmented Dickey-Fuller Test Equation Dependent Variable: D(EXR,2) Method: Least Squares Date: 11/13/17 Time: 17:33 Sample (adjusted): 1983 2015 Included observations: 33 after adjustments


D(EXR(-1)) -1.134373 0.197141 -5.754111 0.0000 C 3.023965 4.848609 0.623677 0.5376

@TREND("1981") 0.189032 0.235265 0.803486 0.4280



186

NOX @ LEVEL

Null Hypothesis: NOX has a unit root Exogenous: Constant, Linear Trend Lag Length: 0 (Automatic - based on SIC, maxlag=0)

t-Statistic Prob.*


5% level -3.548490 10% level -3.207094


Augmented Dickey-Fuller Test Equation Dependent Variable: D(NOX) Method: Least Squares Date: 11/13/17 Time: 17:34 Sample (adjusted): 1982 2015 Included observations: 34 after adjustments


NOX(-1) -0.140612 0.084628 -1.661517 0.1067 C -39.66840 42.40886 -0.935380 0.3568

@TREND("1981") 4.929458 2.830958 1.741269 0.0916



187

NOX @ 1ST Difference

Null Hypothesis: D(NOX) has a unit root Exogenous: Constant, Linear Trend Lag Length: 0 (Automatic - based on SIC, maxlag=0)

t-Statistic Prob.*


5% level -3.552973 10% level -3.209642


Augmented Dickey-Fuller Test Equation Dependent Variable: D(NOX,2) Method: Least Squares Date: 11/13/17 Time: 17:35 Sample (adjusted): 1983 2015 Included observations: 33 after adjustments


D(NOX(-1)) -0.949508 0.225248 -4.215383 0.0002 C -2.603856 41.57786 -0.062626 0.9505

@TREND("1981") 1.175586 2.142944 0.548585 0.5874

R-squared 0.383268 Mean dependent var -8.869697 Adjusted R-squared 0.342152 S.D. dependent var 135.9730 S.E. of regression 110.2847 Akaike info criterion 12.33052 Sum squared resid 364881.7 Schwarz criterion 12.46656 Log likelihood -200.4535 Hannan-Quinn criter. 12.37629 F-statistic 9.321735 Durbin-Watson stat 1.720551 Prob(F-statistic) 0.000710


188

MS @ LEVEL

Null Hypothesis: MS has a unit root Exogenous: Constant, Linear Trend Lag Length: 0 (Automatic - based on SIC, maxlag=0)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic 2.347712 1.0000 Test critical values: 1% level -4.252879

5% level -3.548490 10% level -3.207094


Augmented Dickey-Fuller Test Equation Dependent Variable: D(MS) Method: Least Squares Date: 11/13/17 Time: 17:35 Sample (adjusted): 1982 2015 Included observations: 34 after adjustments


MS(-1) 0.061017 0.025990 2.347712 0.0254 C -281.5639 190.3364 -1.479296 0.1492

@TREND("1981") 36.58338 13.36997 2.736235 0.0102

R-squared 0.681731 Mean dependent var 555.4950 Adjusted R-squared 0.661197 S.D. dependent var 779.1103 S.E. of regression 453.4947 Akaike info criterion 15.15594 Sum squared resid 6375382. Schwarz criterion 15.29062 Log likelihood -254.6510 Hannan-Quinn criter. 15.20187 F-statistic 33.20094 Durbin-Watson stat 1.576311 Prob(F-statistic) 0.000000


189

MS @ 1ST Difference

Null Hypothesis: D(MS) has a unit root Exogenous: Constant, Linear Trend Lag Length: 0 (Automatic - based on SIC, maxlag=0)

t-Statistic Prob.*


5% level -3.552973 10% level -3.209642


Augmented Dickey-Fuller Test Equation Dependent Variable: D(MS,2) Method: Least Squares Date: 11/13/17 Time: 17:36 Sample (adjusted): 1983 2015 Included observations: 33 after adjustments


D(MS(-1)) -0.643456 0.169192 -3.803120 0.0007 C -369.8341 200.0876 -1.848361 0.0744

@TREND("1981") 41.73696 13.68469 3.049902 0.0048

R-squared 0.325380 Mean dependent var 36.99061 Adjusted R-squared 0.280406 S.D. dependent var 541.3738 S.E. of regression 459.2415 Akaike info criterion 15.18354 Sum squared resid 6327082. Schwarz criterion 15.31958 Log likelihood -247.5284 Hannan-Quinn criter. 15.22931 F-statistic 7.234749 Durbin-Watson stat 2.220649 Prob(F-statistic) 0.002728


190

Cointegration Result

Date: 11/13/17 Time: 17:37 Sample (adjusted): 1983 2015 Included observations: 33 after adjustments Trend assumption: Linear deterministic trend Series: INF EXR NOX MS Lags interval (in first differences): 1 to 1

Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.545746 53.93312 47.85613 0.0121 At most 1 0.462066 27.89289 29.79707 0.0816 At most 2 0.196836 7.432258 15.49471 0.5279 At most 3 0.006006 0.198781 3.841466 0.6557

Trace test indicates 1 cointegratingeqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized Max-Eigen 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None 0.545746 26.04024 27.58434 0.0777 At most 1 0.462066 20.46063 21.13162 0.0618 At most 2 0.196836 7.233477 14.26460 0.4619 At most 3 0.006006 0.198781 3.841466 0.6557

Max-eigenvalue test indicates no cointegration at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I):

INF EXR NOX MS -0.039277 -0.002680 -0.025596 0.002113 -0.066668 -0.011766 0.018968 -0.001459 0.009794 0.003182 0.023234 -0.001418 -0.013233 0.028414 0.023952 -0.001985

Unrestricted Adjustment Coefficients (alpha):

D(INF) 4.313642 6.189477 -1.159126 -0.744684 D(EXR) -4.943589 -1.137493 0.763782 -0.730887


191

D(NOX) 18.87760 -13.31736 -43.70874 1.285273 D(MS) 306.1702 -162.8379 -1.755227 -0.319643

1 Cointegrating Equation(s): Log likelihood -694.4932

Normalized cointegrating coefficients (standard error in parentheses) INF EXR NOX MS

1.000000 0.068230 0.651669 -0.053798 (0.13527) (0.20529) (0.01576)

Adjustment coefficients (standard error in parentheses) D(INF) -0.169428

(0.10625) D(EXR) 0.194170

(0.08108) D(NOX) -0.741458

(0.78048) D(MS) -12.02549

(2.78188)



1.000000 0.000000 1.241703 -0.101496 (0.30891) (0.02327)

0.000000 1.000000 -8.647759 0.699076 (2.05988) (0.15519)

Adjustment coefficients (standard error in parentheses) D(INF) -0.582069 -0.084386

(0.18792) (0.02931) D(EXR) 0.270005 0.026632

(0.15883) (0.02477) D(NOX) 0.146385 0.106103

(1.52475) (0.23779) D(MS) -1.169388 1.095460

(4.91478) (0.76648)



1.000000 0.000000 0.000000 -0.016275 0.000000 1.000000 0.000000 0.105565 0.000000 0.000000 1.000000 -0.068632

Adjustment coefficients (standard error in parentheses) D(INF) -0.593421 -0.088074 -0.019942

(0.18862) (0.03018) (0.09536) D(EXR) 0.277485 0.029062 0.122705

(0.15969) (0.02555) (0.08073) D(NOX) -0.281707 -0.032981 -1.751329

(1.38984) (0.22239) (0.70263)


192

D(MS) -1.186579 1.089875 -10.96609 (4.95393) (0.79267) (2.50445)

VEC Result

Vector Error Correction Estimates Date: 11/13/17 Time: 17:39 Sample (adjusted): 1983 2015 Included observations: 33 after adjustments Standard errors in ( ) & t-statistics in [ ]

CointegratingEq: CointEq1

INF(-1) 1.000000

EXR(-1) 0.068230 (0.13527) [ 0.50440]

NOX(-1) 0.651669 (0.20529) [ 3.17435]

MS(-1) -0.053798 (0.01576) [-3.41313]

C 23.91773

Error Correction: D(INF) D(EXR) D(NOX) D(MS)

CointEq1 -0.169428 0.194170 -0.741458 -12.02549 (0.10625) (0.08108) (0.78048) (2.78188) [-1.59468] [ 2.39480] [-0.95000] [-4.32280]

D(INF(-1)) 0.133729 -0.035244 0.286179 3.414265 (0.07885) (0.13649) (1.31385) (4.68295) [ 4.635326] [-0.25822] [ 0.21782] [ 0.72908]

D(EXR(-1)) -0.537755 -0.199282 1.303101 5.719473 (0.25157) (0.19198) (1.84806) (6.58703) [-2.13758] [-1.03802] [ 0.70512] [ 0.86829]

D(NOX(-1)) 0.055020 -0.096784 0.376776 6.416230 (0.05280) (0.04029) (0.38785) (1.38240) [ 1.04211] [-2.40212] [ 0.97146] [ 4.64138]

D(MS(-1)) -0.018296 0.024955 -0.068327 -0.545596 (0.01155) (0.00881) (0.08483) (0.30236) [-1.58433] [ 2.83170] [-0.80545] [-1.80444]

C 10.88961 -3.842824 39.66074 653.2900


193

(5.75456) (4.39151) (42.2731) (150.674) [ 1.89234] [-0.87506] [ 0.93820] [ 4.33578]

R-squared 0.737200 0.260992 0.051309 0.773287 Adj. R-squared 0.695941 0.124139 -0.124375 0.731303 Sum sq. resids 6519.600 3796.857 351824.0 4469649. S.E. equation 15.53920 11.85851 114.1513 406.8692 F-statistic 1.679183 1.907093 0.292053 18.41865 Log likelihood -134.0450 -125.1244 -199.8522 -241.7941 Akaike AIC 8.487574 7.946935 12.47589 15.01783 Schwarz SC 8.759666 8.219027 12.74798 15.28992 Mean dependent 0.094485 5.791836 20.01515 572.2882 S.D. dependent 16.34294 12.67105 107.6528 784.9165

Determinant resid covariance (dof adj.) 4.99E+13 Determinant resid covariance 2.24E+13 Log likelihood -694.4932 Akaike information criterion 43.78747 Schwarz criterion 45.05723


194

System Equation

System: UNTITLED Estimation Method: Least Squares Date: 11/14/17 Time: 10:55 Sample: 1983 2015 Included observations: 33 Total system (balanced) observations 132

Coefficient Std. Error t-Statistic Prob.

C(1) -0.169428 0.106246 -1.594676 0.1137 C(2) 0.133729 0.028852 4.635325 0.0000 C(3) -0.537755 0.251573 -2.137575 0.0348 C(4) 0.055020 0.052797 1.042109 0.2997 C(5) -0.018296 0.011548 -1.584329 0.1160 C(6) 10.88961 5.754560 1.892344 0.0611 C(7) 0.194170 0.081080 2.394799 0.0184 C(8) -0.035244 0.136488 -0.258223 0.7967 C(9) -0.199282 0.191984 -1.038016 0.3016

C(10) -0.096784 0.040291 -2.402116 0.0180 C(11) 0.024955 0.008813 2.831702 0.0055 C(12) -3.842824 4.391509 -0.875058 0.3835 C(13) -0.741458 0.780484 -0.949999 0.3442 C(14) 0.286179 1.313849 0.217817 0.8280 C(15) 1.303101 1.848058 0.705119 0.4823 C(16) 0.376776 0.387846 0.971458 0.3335 C(17) -0.068327 0.084831 -0.805451 0.4223 C(18) 39.66074 42.27312 0.938202 0.3502 C(19) -12.02549 2.781877 -4.322798 0.0000 C(20) 3.414265 4.682948 0.729084 0.4675 C(21) 5.719473 6.587029 0.868293 0.3872 C(22) 6.416230 1.382398 4.641376 0.0000 C(23) -0.545596 0.302364 -1.804436 0.0739 C(24) 653.2900 150.6740 4.335784 0.0000

Determinant residual covariance 2.24E+13

Equation: D(INF) = C(1)*( INF(-1) + 0.0682297244465*EXR(-1) + 0.651669111353*NOX(-1) - 0.0537978917667*MS(-1) + 23.9177290848 ) + C(2)*D(INF(-1)) + C(3)*D(EXR(-1)) + C(4)*D(NOX( -1)) + C(5)*D(MS(-1)) + C(6) Observations: 33 R-squared 0.237200 Mean dependent var 0.094485 Adjusted R-squared 0.095941 S.D. dependent var 16.34294 S.E. of regression 15.53920 Sum squared resid 6519.600


195

Durbin-Watson stat 2.003450

Equation: D(EXR) = C(7)*( INF(-1) + 0.0682297244465*EXR(-1) + 0.651669111353*NOX(-1) - 0.0537978917667*MS(-1) + 23.9177290848 ) + C(8)*D(INF(-1)) + C(9)*D(EXR(-1)) + C(10)*D(NOX( -1)) + C(11)*D(MS(-1)) + C(12) Observations: 33 R-squared 0.260992 Mean dependent var 5.791836 Adjusted R-squared 0.124139 S.D. dependent var 12.67105 S.E. of regression 11.85851 Sum squared resid 3796.857 Durbin-Watson stat 2.015349

Equation: D(NOX) = C(13)*( INF(-1) + 0.0682297244465*EXR(-1) + 0.651669111353*NOX(-1) - 0.0537978917667*MS(-1) + 23.9177290848 ) + C(14)*D(INF(-1)) + C(15)*D(EXR(-1)) + C(16) *D(NOX(-1)) + C(17)*D(MS(-1)) + C(18) Observations: 33 R-squared 0.051309 Mean dependent var 20.01515 Adjusted R-squared -0.124375 S.D. dependent var 107.6528 S.E. of regression 114.1513 Sum squared resid 351824.0 Durbin-Watson stat 1.703052

Equation: D(MS) = C(19)*( INF(-1) + 0.0682297244465*EXR(-1) + 0.651669111353*NOX(-1) - 0.0537978917667*MS(-1) + 23.9177290848 ) + C(20)*D(INF(-1)) + C(21)*D(EXR(-1)) + C(22) *D(NOX(-1)) + C(23)*D(MS(-1)) + C(24) Observations: 33 R-squared 0.773287 Mean dependent var 572.2882 Adjusted R-squared 0.731303 S.D. dependent var 784.9165 S.E. of regression 406.8692 Sum squared resid 4469650. Durbin-Watson stat 1.856473

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